Provisioning Green Energy for Base Stations in Heterogeneous Networks

TL;DR

This paper addresses the challenge of reducing the capital expenditure of deploying green energy systems in cellular base stations by proposing an optimization framework that balances energy costs and QoS requirements.

Contribution

It introduces the green energy provisioning problem and proposes a novel solution combining load balancing and system sizing algorithms to minimize CAPEX.

Findings

The proposed solution effectively reduces green energy deployment costs.

Simulation results confirm the analytical findings and demonstrate viability.

The approach ensures QoS while optimizing green energy system sizing.

Abstract

Cellular networks are among the major energy hoggers of communication networks, and their contributions to the global energy consumption increase rapidly due to the surges of data traffic. With the development of green energy technologies, base stations (BSs) can be powered by green energy in order to reduce the on-grid energy consumption, and subsequently reduce the carbon footprints. However, equipping a BS with a green energy system incurs additional capital expenditure (CAPEX) that is determined by the size of the green energy generator, the battery capacity, and other installation expenses. In this paper, we introduce and investigate the green energy provisioning (GEP) problem which aims to minimize the CAPEX of deploying green energy systems in BSs while satisfying the QoS requirements of cellular networks. The GEP problem is challenging because it involves the optimization over multiple time slots and across multiple BSs. We decompose the GEP problem into the weighted energy minimization problem and the green energy system sizing problem, and propose a green energy provisioning solution consisting of the provision cost aware traffic load balancing algorithm and the binary energy system sizing algorithm to solve the sub-problems and subsequently solve the GEP problem. We validate the performance and the viability of the proposed green energy provisioning solution through extensive simulations, which also conform to our analytically results.